We introduce DiscoTrace, a method to identify the rhetorical strategies that answerers use when responding to information-seeking questions. DiscoTrace represents answers as a sequence of question-related discourse acts paired with interpretations of the original question, annotated on top of rhetorical structure theory parses. Applying DiscoTrace to answers from nine different human communities reveals that communities have diverse preferences for answer construction. In contrast, LLMs do not exhibit rhetorical diversity in their answers, even when prompted to mimic specific human community answering guidelines. LLMs also systematically opt for breadth, addressing interpretations of questions that human answerers choose not to address. Our findings can guide the development of pragmatic LLM answerers that consider a range of strategies informed by context in QA.
DiscoTrace：在信息寻求问答中表示和比较人类与大型语言模型的回答策略 / DiscoTrace: Representing and Comparing Answering Strategies of Humans and LLMs in Information-Seeking Question Answering
这篇论文提出了一个名为DiscoTrace的方法，用于分析和比较人类与大型语言模型在回答信息寻求问题时所使用的修辞策略，发现人类不同群体有各自偏好的回答方式，而大型语言模型则缺乏这种多样性，倾向于提供更宽泛但可能不切题的答案。
源自 arXiv: 2604.15140
